Reducing processing requirements to correct for bias in ratings data having interdependencies among demographic statistics

ABSTRACT

Examples apparatus disclosed herein are to determine a plurality of weights based on a data structure having elements corresponding to pairings of ones of a plurality of demographic partition statistics and ones of a plurality of baseline demographic statistics obtained for a target population, the demographic partition statistics corresponding to a plurality of demographic partitions of a sample population, a first element of the data structure to combine a first one of the demographic partition statistics with a first one of the baseline demographic statistics of the target population based on a first value corresponding to a numerator term of an expression and a second value corresponding to a denominator term of the expression, the weights corresponding respectively to the demographic partitions of the sample population. Disclosed example apparatus are also to adjust the attribute data based on the weights to determine ratings data for the target population.

RELATED APPLICATION(S)

This patent arises from a continuation of U.S. patent application Ser.No. 14/835,401 (now U.S. Pat. No. ______), which is titled “REDUCINGPROCESSING REQUIREMENTS TO CORRECT FOR BIAS IN RATINGS DATA HAVINGINTERDEPENDENCIES AMONG DEMOGRAPHIC STATISTICS,” and which was filed onAug. 25, 2015, which claims the benefit of U.S. Provisional ApplicationSer. No. 62/192,919, which is titled “REDUCING PROCESSING REQUIREMENTSTO CORRECT FOR BIAS IN RATINGS DATA HAVING INTERDEPENDENCIES AMONGDEMOGRAPHIC STATISTICS,” and which was filed on Jul. 15, 2015. Priorityto U.S. patent application Ser. No. 14/835,401 and U.S. ProvisionalApplication Ser. No. 62/192,919 is claimed. U.S. patent application Ser.No. 14/835,401 and U.S. Provisional Application Ser. No. 62/192,919 arehereby incorporated herein by reference in their respective entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moreparticularly, to reducing processing requirements to correct for bias inratings data having interdependencies among demographic statistics.

BACKGROUND

Traditionally, audience measurement entities determine compositions ofaudiences exposed to media by monitoring registered panel members andextrapolating their behavior onto a larger, target population ofinterest. That is, an audience measurement entity enrolls people thatconsent to being monitored into a panel and collects relatively highlyaccurate demographic information from those panel members via, forexample, in-person, telephonic, and/or online interviews. The audiencemeasurement entity then monitors those panel members to determine mediaexposure information describing media (e.g., television programs, radioprograms, movies, streaming media, etc.) exposed to those panel members.By combining the media exposure information with the demographicinformation for the panel members, and extrapolating the result to thetarget population, the audience measurement entity can determinedetailed ratings data identifying, for example, targeted demographicmarkets for different media. However, the composition of a panel mayover-represent and/or under-represent different demographic groups ofthe target population, thereby leading to bias in the resulting ratingsdata.

More recent techniques employed by audience measurement entities tomonitor exposure to Internet accessible media or, more generally, onlinemedia attempt to reduce such bias by expanding the available set ofmonitored individuals to a sample population that may includeindividuals who are registered panel members, as well as individuals whoare not registered panel members. In some such techniques, demographicinformation for the monitored individuals can be obtained from one ormore database proprietors (e.g., social network sites, multi-servicesites, online retailer sites, credit services, etc.) with which theindividuals subscribe to receive one or more online services. Theaudience measurement entity may then combine media exposure informationwith the demographic information obtained for these monitoredindividuals to determine ratings data for the sample population.Although such a sample population is typically larger than the panelsemployed by audience measurement entities, the resulting ratings datadetermined for the sample population may still have demographicstatistics exhibiting bias relative to the corresponding demographicstatistics of the target population.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates example client devices that report audienceimpressions for Internet-based media to impression collection entitiesto facilitate identifying numbers of impressions and sizes of audiencesexposed to different Internet-based media.

FIG. 2 is an example communication flow diagram illustrating an examplemanner in which an example audience measurement entity and an exampledatabase proprietor can collect impressions and demographic informationassociated with a client device, and can further correct for bias inratings data having interdependencies among demographic statistics inaccordance with the teachings of this disclosure.

FIG. 3 is a block diagram of an example ratings bias corrector that maybe included in the example audience measurement entity of FIGS. 1 and/or2 to correct for bias in ratings data having interdependencies amongdemographic statistics in accordance with the teachings of thisdisclosure.

FIG. 4 is a block diagram of an example strata weight determiner thatmay be used to implement the example ratings bias corrector of FIG. 3

FIGS. 5-7 are flowcharts representative of example machine readableinstructions that may be executed to implement the example ratings biascorrector of FIG. 3 and/or the example strata weight determiner of FIG.4.

FIG. 8 is a block diagram of an example processor platform structured toexecute the example machine readable instructions of FIGS. 5, 6 and/or 7to implement the example ratings bias corrector of FIG. 3 and/or theexample strata weight determiner of FIG. 4.

Wherever possible, the same reference numbers will be used throughoutthe drawing(s) and accompanying written description to refer to the sameor like parts, elements, etc.

DETAILED DESCRIPTION

Methods, apparatus, systems and articles of manufacture (e.g., physicalstorage media) to correct for bias in ratings data havinginterdependencies among demographic statistics are disclosed herein. Asnoted above, ratings data determined for a sample population may havedemographic statistics that exhibit bias relative to the correspondingdemographic statistics of a target population to which the ratings datais to be applied. For example, some ratings campaigns (e.g., such asaudience measurement campaigns, market research campaigns, etc.) dividea sample population into several different demographic categories (e.g.,such as categories based on gender, age, income, mobile phone usage,etc.), and an individual in the population may belong to multiple ofthese demographic categories. A given ratings campaign may then involvedetermining ratings data (e.g., media exposure ratings data and/or otheraudience measurement data, market research ratings data, etc.) forindividual demographic categories or combinations of such demographiccategories for a sample population, and applying those ratings to adifferent (e.g., larger) target population. However, due toover-sampling and/or under-sampling of individuals belonging to thedifferent demographic categories, the demographic statistics of thesample population may be biased relative to the correspondingdemographic statistics of the target population.

Some prior audience measurement and/or market research systems (referredto herein as audience measurement systems) may employ simple scaling tocorrect for the bias between the demographic statistics of the samplepopulation and the demographic statistics of the target population. Suchsimple scaling may involve emphasizing or deemphasizing the contributionof an individual to the sample population's ratings data by a scalefactor chosen to adjust one particular demographic statistic of thesample population to match the corresponding demographic statistic oftarget population. However, such simple scaling does not account for therelationships between different demographic statistics. For example, ademographic statistic representing the probability of an individualbeing a mobile phone user given the individual is male is related to ademographic statistic representing the probability of an individualbeing male, as well as to a demographic statistic representing theprobability of an individual being male and being a mobile phone user.Determining a scale factor that adjusts just one of those demographicstatistics to account for a bias between the sample population andtarget population could exacerbate the bias between the other, relateddemographic statistics.

Accordingly, such prior techniques for correcting for bias betweensample population and target population demographic statistics mayrequire multiple processing iterations to settle on a scale factor thatreduces the bias between the sample population statistics and the targetpopulation statistic of interest without unduly impacting the biasbetween the other, related demographic statistics. Such processingiterations may involve successively varying the scale factor andevaluating the resulting bias between the sample population statisticand target population statistic of interest, as well as the resultingimpact on the biases between the other, related demographic statistics.In some examples, such iterative processing associated with priortechniques may not be able to yield a scale factor that both reduces thebias between the sample population statistics and the target populationstatistic of interest, and does not unduly impact the bias between theother, related demographic statistics.

Unlike such prior audience measurement systems, example methods,apparatus, systems and articles of manufacture (e.g., physical storagemedia) disclosed herein implement technical solutions to addresstechnical problems associated with correcting for bias between thedemographic statistics of a sample population and the correspondingdemographic statistics of a target population when there areinterdependencies between the different demographic statistics. Morespecifically, example technical solutions disclosed herein eliminate theneed to successively iterate through scale factor adjustments bydetermining a set of weights that reduces the bias, as a whole, betweena set of baseline demographic statistics determined for a samplepopulation and a corresponding set of baseline demographic statisticsknown for a target population. Example technical solutions disclosedherein can then adjust (e.g., weight) demographic attributes associatedwith individuals of the sample population based on this set of weightsto determine ratings data for the sample population that can be appliedto the target population.

Advantageously, these example technical solutions reduce (or eliminate)the need to iterate through different adjustment values (e.g., the priorscale factors) to adjust the bias for interdependent demographicstatistics. Instead of multiple iterations, example technical solutionsdisclosed herein can determine the set of weights that reduces the bias,as a whole, for interdependent statistics in a single processing pass,thereby improving processor efficiency. Furthermore, because the singleprocessing iteration can determine a set of weights that reduces bias,as a whole, for interdependent statistics, such disclosed exampletechnical solutions avoid the further inefficiencies of priortechniques, which may need to perform subsequent processing iterationsto correct for errors (e.g., increased bias among some interdependentstatistics) introduced by prior processing iterations.

For example, some example methods disclosed herein to correct for biasin ratings data include accessing information specifying a set ofpopulation strata (e.g., population partitions) having correspondingstrata demographic statistics capable of being combined to determine afirst set of baseline demographic statistics for a sample populationthat corresponds to a second set of baseline demographic statistics fora target population. Such disclosed example methods also includedetermining (e.g., in a first processing pass or, in other words,without the need to perform multiple processing iterations to correctfor errors (e.g., increased biases) introduced by prior processingiterations), a set of weights corresponding respectively to the set ofpopulation strata that reduces bias between the second set of baselinedemographic statistics for the target population and the first set ofbaseline demographic statistics for the sample population when the setof weights is applied to the strata demographic statistics to determinethe first set of baseline demographic statistics for the samplepopulation. Such disclosed example methods further include adjusting,based on the set of weights, attributes associated with individuals ofthe sample population to determine the ratings data.

In some disclosed examples, the population strata form a mutuallyexclusive set that covers the sample population. Additionally oralternatively, in some disclosed examples, the strata demographicstatistics represent numbers of individuals from the sample populationbelonging to respective ones of the set of population strata.

Additionally or alternatively, in some disclosed example methods,determining the set of weights includes forming a matrix having matrixelements corresponding to pairings of ones of the strata demographicstatistics and ones of the first set of baseline demographic statistics.For example, a first matrix element corresponding to a pairing of afirst one of the strata demographic statistics and a first one of thefirst set of baseline demographic statistics may have a value based on acontribution of the first one of the strata demographic statistics tocomputation of the first one of the first set of baseline demographicstatistics. Such disclosed example methods also include determining theset of weights based on the matrix. For example, some such disclosedexample methods determine the set of weights to minimize an expressionbased on multiplying the matrix and a vector of the weights, but subjectto a constraint that the weights have values greater than or equal toone.

Additionally or alternatively, in some disclosed example methods inwhich a first individual of the sample population is included in a firstone of the population strata and a second individual of the samplepopulation is included in a second one of the population strata, theexample methods include scaling a contribution of an attributeassociated with the first individual by a first one of the weightscorresponding to the first one of the population strata to determine afirst scaled attribute contribution. Some such disclosed example methodsalso include scaling a contribution of an attribute associated with thesecond individual by a second one of the weights corresponding to thesecond one of the population strata to determine a second scaledattribute contribution. Some such disclosed example methods furtherinclude combining the first scaled attribute contribution and the secondscaled attribute contribution to determine the ratings data.

Additionally or alternatively, some disclosed example methods furtherinclude determining the strata demographic statistics from impressionscollected by the processor in response to beacon request messagesreceived by at least one of an audience measurement entity or a databaseprovider. Additionally or alternatively, some disclosed example methodsfurther include transmitting the ratings data at least one ofelectronically or optically from the processor to a receiving device viaa network.

These and other example methods, apparatus, systems and articles ofmanufacture (e.g., physical storage media) to correct for bias inratings data having interdependencies among demographic statistics aredisclosed in greater detail below.

Turning to the figures, FIG. 1 illustrates example client devices 102that report audience impressions for online (e.g., Internet-based) mediato impression collection entities 104 to facilitate determining numbersof impressions and sizes of audiences exposed to different online media.An impression generally refers to an instance of an individual's (orhousehold's) exposure to media (e.g., content, advertising, etc.). Asused herein, the term impression collection entity refers to any entitythat collects impression data, such as, for example, audiencemeasurement entities and database proprietors that collect impressiondata.

The client devices 102 of the illustrated example may be any devicecapable of accessing media over a network. For example, the clientdevices 102 may be a computer, a tablet, a mobile device, a smarttelevision, or any other Internet-capable device or appliance. Examplesdisclosed herein may be used to collect impression information for anytype of media, including content and/or advertisements. Media mayinclude advertising and/or content delivered via web pages, streamingvideo, streaming audio, Internet protocol television (IPTV), movies,television, radio and/or any other vehicle for delivering media. In someexamples, media includes user-generated media that is, for example,uploaded to media upload sites, such as YouTube, and subsequentlydownloaded and/or streamed by one or more other client devices forplayback. Media may also include advertisements. Advertisements aretypically distributed with content (e.g., programming). Traditionally,content is provided at little or no cost to the audience because it issubsidized by advertisers that pay to have their advertisementsdistributed with the content. As used herein, “media” referscollectively and/or individually to content and/or advertisement(s).

In the illustrated example, the client devices 102 employ web browsersand/or applications (e.g., apps) to access media, some of which includeinstructions that cause the client devices 102 to report mediamonitoring information to one or more of the impression collectionentities 104. That is, when a client device 102 of the illustratedexample accesses media, a web browser and/or application of the clientdevice 102 executes one or more instructions (e.g., beaconinstruction(s)) in the media, which cause the client device 102 to senda beacon request or impression request 108 to one or more impressioncollection entities 104 via, for example, the Internet 110. The beaconrequests 108 of the illustrated example include information aboutaccesses to media at the corresponding client device(s) 102 generatingthe beacon requests. Such beacon requests allow monitoring entities,such as the impression collection entities 104, to collect impressionsfor different media accessed via the client devices 102. In this manner,the impression collection entities 104 can generate large impressionquantities for different media (e.g., different content and/oradvertisement campaigns). Examples techniques for using beaconinstructions and beacon requests to cause devices to collect impressionsfor different media accessed via client devices are further disclosed inat least U.S. Pat. No. 6,108,637 to Blumenau and U.S. Pat. No. 8,370,489to Mainak, et al., which are incorporated herein by reference in theirrespective entireties.

The impression collection entities 104 of the illustrated exampleinclude an example audience measurement entity (AME) 114 and an exampledatabase proprietor (DP) 116. In the illustrated example, the AME 114does not provide the media to the client devices 102 and is a trusted(e.g., neutral) third party (e.g., The Nielsen Company, LLC) forproviding accurate media access statistics. In the illustrated example,the database proprietor 116 is one of many database proprietors thatoperate on the Internet to provide services to large numbers ofsubscribers. Such services may include, but are not limited to, emailservices, social networking services, news media services, cloud storageservices, streaming music services, streaming video services, onlineretail shopping services, credit monitoring services, etc. Exampledatabase proprietors include social network sites (e.g., Facebook,Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google,etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), creditservices (e.g., Experian), and/or any other web service(s) site thatmaintains user registration records. In examples disclosed herein, thedatabase proprietor 116 maintains user account records corresponding tousers registered for Internet-based services provided by the databaseproprietors. That is, in exchange for the provision of services,subscribers register with the database proprietor 116. As part of thisregistration, the subscribers provide detailed demographic informationto the database proprietor 116. Demographic information may include, forexample, gender, age, ethnicity, income, home location, education level,occupation, etc. In the illustrated example, the database proprietor 116sets a device/user identifier (e.g., an identifier described below inconnection with FIG. 2) on a subscriber's client device 102 that enablesthe database proprietor 116 to identify the subscriber.

In the illustrated example, when the database proprietor 116 receives abeacon/impression request 108 from a client device 102, the databaseproprietor 116 requests the client device 102 to provide the device/useridentifier that the database proprietor 116 had previously set for theclient device 102. The database proprietor 116 uses the device/useridentifier corresponding to the client device 102 to identifydemographic information in its user account records corresponding to thesubscriber of the client device 102. In this manner, the databaseproprietor 116 can generate demographic impressions by associatingdemographic information with an audience impression for the mediaaccessed at the client device 102. Thus, as used herein, a demographicimpression is an impression that is associated with a characteristic(e.g., a demographic characteristic) of the person exposed to the media.Through the use of demographic impressions, which associate monitored(e.g., logged) impressions with demographic information, it is possibleto measure media exposure and, by extension, infer media consumptionbehaviors across different demographic classifications (e.g., groups) ofa sample population of individuals.

In the illustrated example, the AME 114 establishes a panel of users whohave agreed to provide their demographic information and to have theirInternet browsing activities monitored. When an individual joins the AMEpanel, the person provides detailed information concerning the person'sidentity and demographics (e.g., gender, age, ethnicity, income, homelocation, occupation, etc.) to the AME 114. The AME 114 sets adevice/user identifier (e.g., an identifier described below inconnection with FIG. 2) on the person's client device 102 that enablesthe AME 114 to identify the panelist.

In the illustrated example, when the AME 114 receives a beacon request108 from a client device 102, the AME 114 requests the client device 102to provide the AME 114 with the device/user identifier the AME 114previously set for the client device 102. The AME 114 uses thedevice/user identifier corresponding to the client device 102 toidentify demographic information in its user AME panelist recordscorresponding to the panelist of the client device 102. In this manner,the AME 114 can generate demographic impressions by associatingdemographic information with an audience impression for the mediaaccessed at the client device 102.

In the illustrated example, the database proprietor 116 reportsdemographic impression data to the AME 114. To preserve the anonymity ofits subscribers, the demographic impression data may be anonymousdemographic impression data and/or aggregated demographic impressiondata. In the case of anonymous demographic impression data, the databaseproprietor 116 reports user-level demographic impression data (e.g.,which is resolvable to individual subscribers), but with any personalidentification information removed from or obfuscated (e.g., scrambled,hashed, encrypted, etc.) in the reported demographic impression data.For example, anonymous demographic impression data, if reported by thedatabase proprietor 116 to the AME 114, may include respectivedemographic impression data for each device 102 from which a beaconrequest 108 was received, but with any personal identificationinformation removed from or obfuscated in the reported demographicimpression data. In the case of aggregated demographic impression data,individuals are grouped into different demographic classifications, andaggregate demographic impression data (e.g., which is not resolvable toindividual subscribers) for the respective demographic classificationsis reported to the AME 114. For example, aggregate demographicimpression data, if reported by the database proprietor 116 to the AME114, may include first demographic impression data aggregated fordevices 102 associated with demographic information belonging to a firstdemographic classification (e.g., a first age group, such as a groupwhich includes ages less than 18 years old), second demographicimpression data for devices 102 associated with demographic informationbelonging to a second demographic classification (e.g., a second agegroup, such as a group which includes ages from 18 years old to 34 yearsold), etc.

In the illustrated example, the AME 114 includes an example rating biascorrector 120 to determine ratings data from the demographic impressionscollected by the AME 114 and/or the database proprietor 116. The ratingsdata determined by the example rating bias corrector 120 can be any typeof ratings data, such as ratings data quantifying (e.g., in terms ofaudience size, number of households, number of impressions, etc.) mediaexposure across different demographic classifications (e.g., groups) ofthe sample population. Moreover, and as disclosed in further detailbelow, the rating bias corrector 120 of the illustrated example correctsfor bias in the ratings data in accordance with the teachings of thisdisclosure.

FIG. 2 is an example communication flow diagram 200 illustrating anexample manner in which the AME 114 and the database proprietor 116 cancollect demographic impressions based on client devices 102 reportingimpressions to the AME 114 and the database proprietor 116. FIG. 2 alsoshows the example rating bias corrector 120, which is able to correctfor bias in ratings data having interdependencies among demographicstatistics in accordance with the teachings of this disclosure. Theexample chain of events shown in FIG. 2 occurs when a client device 102accesses media for which the client device 102 reports an impression tothe AME 114 and/or the database proprietor 116. In some examples, theclient device 102 reports impressions for accessed media based oninstructions (e.g., beacon instructions) embedded in the media thatinstruct the client device 102 (e.g., that instruct a web browser or anapp in the client device 102) to send beacon/impression requests (e.g.,the beacon/impression requests 108 of FIG. 1) to the AME 114 and/or thedatabase proprietor 116. In such examples, the media having the beaconinstructions is referred to as tagged media. In other examples, theclient device 102 reports impressions for accessed media based oninstructions embedded in apps or web browsers that execute on the clientdevice 102 to send beacon/impression requests (e.g., thebeacon/impression requests 108 of FIG. 1) to the AME 114 and/or thedatabase proprietor 116 for corresponding media accessed via those appsor web browsers. In some examples, the beacon/impression requests (e.g.,the beacon/impression requests 108 of FIG. 1) include device/useridentifiers (e.g., AME IDs and/or DP IDs) as described further below toallow the corresponding AME 114 and/or the corresponding databaseproprietor 116 to associate demographic information with resultinglogged impressions.

In the illustrated example, the client device 102 accesses media 206that is tagged with beacon instructions 208. The beacon instructions 208cause the client device 102 to send a beacon/impression request 212 toan AME impressions collector 218 when the client device 102 accesses themedia 206. For example, a web browser and/or app of the client device102 executes the beacon instructions 208 in the media 206 which instructthe browser and/or app to generate and send the beacon/impressionrequest 212. In the illustrated example, the client device 102 sends thebeacon/impression request 212 using an HTTP (hypertext transferprotocol) request addressed to the URL (uniform resource locator) of theAME impressions collector 218 at, for example, a first Internet domainof the AME 114. The beacon/impression request 212 of the illustratedexample includes a media identifier 213 (e.g., an identifier that can beused to identify content, an advertisement, and/or any other media)corresponding to the media 206. In some examples, the beacon/impressionrequest 212 also includes a site identifier (e.g., a URL) of the websitethat served the media 206 to the client device 102 and/or a host websiteID (e.g., www.acme.com) of the website that displays or presents themedia 206. In the illustrated example, the beacon/impression request 212includes a device/user identifier 214. In the illustrated example, thedevice/user identifier 214 that the client device 102 provides to theAME impressions collector 218 in the beacon impression request 212 is anAME ID because it corresponds to an identifier that the AME 114 uses toidentify a panelist corresponding to the client device 102. In otherexamples, the client device 102 may not send the device/user identifier214 until the client device 102 receives a request for the same from aserver of the AME 114 in response to, for example, the AME impressionscollector 218 receiving the beacon/impression request 212.

In some examples, the device/user identifier 214 may be a deviceidentifier (e.g., an international mobile equipment identity (IMEI), amobile equipment identifier (MEID), a media access control (MAC)address, etc.), a web browser unique identifier (e.g., a cookie), a useridentifier (e.g., a user name, a login ID, etc.), an Adobe Flash® clientidentifier, identification information stored in an HTML5 datastore(where HTML is an abbreviation for hypertext markup language), and/orany other identifier that the AME 114 stores in association withdemographic information about users of the client devices 102. In thismanner, when the AME 114 receives the device/user identifier 214, theAME 114 can obtain demographic information corresponding to a user ofthe client device 102 based on the device/user identifier 214 that theAME 114 receives from the client device 102. In some examples, thedevice/user identifier 214 may be encrypted (e.g., hashed) at the clientdevice 102 so that only an intended final recipient of the device/useridentifier 214 can decrypt the hashed identifier 214. For example, ifthe device/user identifier 214 is a cookie that is set in the clientdevice 102 by the AME 114, the device/user identifier 214 can be hashedso that only the AME 114 can decrypt the device/user identifier 214. Ifthe device/user identifier 214 is an IMEI number, the client device 102can hash the device/user identifier 214 so that only a wireless carrier(e.g., the database proprietor 116) can decrypt the hashed identifier214 to recover the IMEI for use in accessing demographic informationcorresponding to the user of the client device 102. By hashing thedevice/user identifier 214, an intermediate party (e.g., an intermediateserver or entity on the Internet) receiving the beacon request cannotdirectly identify a user of the client device 102.

In response to receiving the beacon/impression request 212, the AMEimpressions collector 218 logs an impression for the media 206 bystoring the media identifier 213 contained in the beacon/impressionrequest 212. In the illustrated example of FIG. 2, the AME impressionscollector 218 also uses the device/user identifier 214 in thebeacon/impression request 212 to identify AME panelist demographicinformation corresponding to a panelist of the client device 102. Thatis, the device/user identifier 214 matches a user ID of a panelistmember (e.g., a panelist corresponding to a panelist profile maintainedand/or stored by the AME 114). In this manner, the AME impressionscollector 218 can associate the logged impression with demographicinformation of a panelist corresponding to the client device 102.

In some examples, the beacon/impression request 212 may not include thedevice/user identifier 214 if, for example, the user of the clientdevice 102 is not an AME panelist. In some such examples, the AMEimpressions collector 218 logs impressions regardless of whether theclient device 102 provides the device/user identifier 214 in thebeacon/impression request 212 (or in response to a request for theidentifier 214). When the client device 102 does not provide thedevice/user identifier 214, the AME impressions collector 218 can stillbenefit from logging an impression for the media 206 even though it doesnot have corresponding demographics. For example, the AME 114 may stilluse the logged impression to generate a total impressions count and/or afrequency of impressions (e.g., an impressions frequency) for the media206. Additionally or alternatively, the AME 114 may obtain demographicsinformation from the database proprietor 116 for the logged impressionif the client device 102 corresponds to a subscriber of the databaseproprietor 116.

In the illustrated example of FIG. 2, to compare or supplement panelistdemographics (e.g., for accuracy or completeness) of the AME 114 withdemographics from one or more database proprietors (e.g., the databaseproprietor 116), the AME impressions collector 218 returns a beaconresponse message 222 (e.g., a first beacon response) to the clientdevice 102 including an HTTP “302 Found” re-direct message and a URL ofa participating database proprietor 116 at, for example, a secondInternet domain. In the illustrated example, the HTTP “302 Found”re-direct message in the beacon response 222 instructs the client device102 to send a second beacon request 226 to the database proprietor 116.In other examples, instead of using an HTTP “302 Found” re-directmessage, redirects may be implemented using, for example, an iframesource instruction (e.g., <iframe src=“ ”>) or any other instructionthat can instruct a client device to send a subsequent beacon request(e.g., the second beacon request 226) to a participating databaseproprietor 116. In the illustrated example, the AME impressionscollector 218 determines the database proprietor 116 specified in thebeacon response 222 using a rule and/or any other suitable type ofselection criteria or process. In some examples, the AME impressionscollector 218 determines a particular database proprietor to which toredirect a beacon request based on, for example, empirical dataindicative of which database proprietor is most likely to havedemographic data for a user corresponding to the device/user identifier214. In some examples, the beacon instructions 208 include a predefinedURL of one or more database proprietors to which the client device 102should send follow up beacon requests 226. In other examples, the samedatabase proprietor is always identified in the first redirect message(e.g., the beacon response 222).

In the illustrated example of FIG. 2, the beacon/impression request 226may include a device/user identifier 227 that is a DP ID because it isused by the database proprietor 116 to identify a subscriber of theclient device 102 when logging an impression. In some instances (e.g.,in which the database proprietor 116 has not yet set a DP ID in theclient device 102), the beacon/impression request 226 does not includethe device/user identifier 227. In some examples, the DP ID is not sentuntil the database proprietor 116 requests the same (e.g., in responseto the beacon/impression request 226). In some examples, the device/useridentifier 227 is a device identifier (e.g., an IMEI), an MEID, a MACaddress, etc.), a web browser unique identifier (e.g., a cookie), a useridentifier (e.g., a user name, a login ID, etc.), an Adobe Flash® clientidentifier, identification information stored in an HTML5 datastore,and/or any other identifier that the database proprietor 116 stores inassociation with demographic information about subscribers correspondingto the client devices 102. In some examples, the device/user identifier227 may be encrypted (e.g., hashed) at the client device 102 so thatonly an intended final recipient of the device/user identifier 227 candecrypt the hashed identifier 227. For example, if the device/useridentifier 227 is a cookie that is set in the client device 102 by thedatabase proprietor 116, the device/user identifier 227 can be hashed sothat only the database proprietor 116 can decrypt the device/useridentifier 227. If the device/user identifier 227 is an IMEI number, theclient device 102 can hash the device/user identifier 227 so that only awireless carrier (e.g., the database proprietor 116) can decrypt thehashed identifier 227 to recover the IMEI for use in accessingdemographic information corresponding to the user of the client device102. By hashing the device/user identifier 227, an intermediate party(e.g., an intermediate server or entity on the Internet) receiving thebeacon request cannot directly identify a user of the client device 102.For example, if the intended final recipient of the device/useridentifier 227 is the database proprietor 116, the AME 114 cannotrecover identifier information when the device/user identifier 227 ishashed by the client device 102 for decrypting only by the intendeddatabase proprietor 116. When the database proprietor 116 receives thedevice/user identifier 227, the database proprietor 116 can obtaindemographic information corresponding to a user of the client device 102based on the device/user identifier 227 that the database proprietor 116receives from the client device 102.

Although only a single database proprietor 116 is shown in FIGS. 1 and2, the impression reporting/collection process of FIGS. 1 and 2 may beimplemented using multiple database proprietors. In some such examples,the beacon instructions 208 cause the client device 102 to sendbeacon/impression requests 226 to numerous database proprietors. Forexample, the beacon instructions 208 may cause the client device 102 tosend the beacon/impression requests 226 to the numerous databaseproprietors in parallel or in daisy chain fashion. In some suchexamples, the beacon instructions 208 cause the client device 102 tostop sending beacon/impression requests 226 to database proprietors oncea database proprietor has recognized the client device 102. In otherexamples, the beacon instructions 208 cause the client device 102 tosend beacon/impression requests 226 to database proprietors so thatmultiple database proprietors can recognize the client device 102 andlog a corresponding impression. Thus, in some examples, multipledatabase proprietors are provided the opportunity to log impressions andprovide corresponding demographics information if the user of the clientdevice 102 is a subscriber of services of those database proprietors.

In some examples, prior to sending the beacon response 222 to the clientdevice 102, the AME impressions collector 218 replaces site IDs (e.g.,URLs) of media provider(s) that served the media 206 with modified siteIDs (e.g., substitute site IDs) which are discernable only by the AME114 to identify the media provider(s). In some examples, the AMEimpressions collector 218 may also replace a host website ID (e.g.,www.acme.com) with a modified host site ID (e.g., a substitute host siteID) which is discernable only by the AME 114 as corresponding to thehost website via which the media 206 is presented. In some examples, theAME impressions collector 218 also replaces the media identifier 213with a modified media identifier 213 corresponding to the media 206. Inthis way, the media provider of the media 206, the host website thatpresents the media 206, and/or the media identifier 213 are obscuredfrom the database proprietor 116, but the database proprietor 116 canstill log impressions based on the modified values (e.g., if suchmodified values are included in the beacon request 226), which can laterbe deciphered by the AME 114 after the AME 114 receives loggedimpressions from the database proprietor 116. In some examples, the AMEimpressions collector 218 does not send site IDs, host site IDS, themedia identifier 213 or modified versions thereof in the beacon response222. In such examples, the client device 102 provides the original,non-modified versions of the media identifier 213, site IDs, host IDs,etc. to the database proprietor 116.

In the illustrated example, the AME impression collector 218 maintains amodified ID mapping table 228 that maps original site IDs with modified(or substitute) site IDs, original host site IDs with modified host siteIDs, and/or maps modified media identifiers to the media identifierssuch as the media identifier 213 to obfuscate or hide such informationfrom database proprietors such as the database proprietor 116. Also inthe illustrated example, the AME impressions collector 218 encrypts allof the information received in the beacon/impression request 212 and themodified information to prevent any intercepting parties from decodingthe information. The AME impressions collector 218 of the illustratedexample sends the encrypted information in the beacon response 222 tothe client device 102 so that the client device 102 can send theencrypted information to the database proprietor 116 in thebeacon/impression request 226. In the illustrated example, the AMEimpressions collector 218 uses an encryption that can be decrypted bythe database proprietor 116 site specified in the HTTP “302 Found”re-direct message.

Periodically or aperiodically, the impression data collected by thedatabase proprietor 116 is provided to a DP impressions collector 232 ofthe AME 114 as, for example, batch data. Additional examples that may beused to implement the beacon instruction processes of FIG. 2 aredisclosed in U.S. Pat. No. 8,370,489 to Mainak et al. In addition, otherexamples that may be used to implement such beacon instructions aredisclosed in U.S. Pat. No. 6,108,637 to Blumenau.

In the example of FIG. 2, the AME 114 includes the example ratings biascorrector 120 to determine ratings data from the impressions collectedby the AME impressions collector 218 and/or obtained by the DPimpressions collector 232, and to correct for bias in such ratings datain accordance with the teachings of this disclosure. A block diagram ofan example implementation of the ratings bias corrector 120 of FIGS. 1and/or 2 is illustrated in FIG. 3.

The example ratings bias corrector 120 of FIG. 3 includes an exampledata interface 305 to interface with the AME impressions collector 218and/or the DP impressions collector 232 to obtain demographic impressiondata specifying population attributes including, for example, mediaexposure data (e.g., such as numbers of impressions for given media,times and/or duration of media exposure, etc.) and/or purchase activity(e.g., such as numbers of products purchased, numbers of servicesaccessed, etc.), etc., as well as demographic data (e.g., such as age,gender, income, location, mobile phone usage, etc.) for individuals in asample population (e.g., such as individuals associated with the devices102 sending the beacon requests 108, 212, 226, etc.). The example datainterface 305 can be implemented by any type(s), number(s) and/orcombination(s) of communication interfaces, network interfaces, etc.,such as the example interface circuit 820 of FIG. 8, which is describedin further detail below.

The example ratings bias corrector 120 of FIG. 3 also includes anexample sample population attributes storage 310 to store the populationattributes included in the demographic impression data collected via theexample data interface 305 for the different individuals in the samplepopulation. The example sample population attributes storage 310 may beimplemented by any number(s) and/or type(s) of volatile and/ornon-volatile memory, storage, etc., or combination(s) thereof, such asthe example volatile memory 814 and/or the example mass storagedevice(s) 828 of FIG. 8, which is described in further detail below.

The example ratings bias corrector 120 of FIG. 3 further includes anexample target population attributes storage 315 to store populationattributes obtained for a target population of individuals. For example,the target population may correspond to a larger population (e.g., anational population) onto which ratings data determined by the ratingsbias corrector 120 for the sample population (e.g., a local population)is to be projected or otherwise applied, or a second population (e.g.,associated with a different region than the sample population) that isto be combined with the sample population for which the ratings biascorrector 120 has obtained demographic impression data, etc. In someexamples, the population attributes stored in the target populationattributes storage 315 for the target population of individuals areobtained from one or more sources, such as one or more surveys, one ormore censuses, another ratings campaign performed by the AME 114 and/ora different AME, etc. The example target population attributes storage315 may be implemented by any number(s) and/or type(s) of volatileand/or non-volatile memory, storage, etc., or combination(s) thereof,such as the example volatile memory 814 and/or the example mass storagedevice(s) 828 of FIG. 8, which is described in further detail below.Furthermore, the example sample population attributes storage 310 andthe example target population attributes storage 315 may be implementedby the same or different volatile and/or non-volatile memory, storage,etc.

The example ratings bias corrector 120 of FIG. 3 also includes anexample baseline statistics determiner 320 to determine a set ofbaseline demographic statistics for the target population from thepopulation attributes stored in the example target population attributesstorage 315. (The term “baseline” is used to indicate that this set ofdemographics statistics is known or otherwise ascertainable for thetarget population.) In the illustrated example of FIG. 3, the set ofbaseline demographic statistics determined by the baseline statisticsdeterminer 320 correspond to probabilities and/or conditionalprobabilities of individuals of the target population belonging todifferent demographic classifications (e.g., different demographicgroups) of interest represented by the population attributes stored inthe example target population attributes storage 315. In some examples,the example baseline statistics determiner 320 determines theprobabilities of the target population individual belonging to differentdemographic classification by counting the number of individuals havingattributes associated with a given demographic classification, and thendividing the count by the total number of individuals in the targetpopulation. In some examples, the example baseline statistics determiner320 further combines such probabilities to determine conditionalprobabilities representing, for example, the probability thatindividuals of the target population belong to one or more demographicclassifications given that the individuals belong to one or more otherdemographic classifications.

Table 1 illustrates an example set of baseline demographic statisticscapable of being determined by the example baseline statisticsdeterminer 320 for a target population (corresponding to the secondcolumn of the table). The example set of baseline demographic statisticsin Table 1 include statistics corresponding to the probability of anindividual being male, P(Male), the probability of an individual beingfemale, P(Female), the probability of an individual being male given theindividual is a mobile phone user, P(Male|Mobile Phone), the probabilityof an individual being female given the individual is a mobile phoneuser, P(Female|Mobile Phone), the probability of an individual being amobile phone user given the individual is male, P(Mobile Phone|Male),and the probability of an individual being a mobile phone user given theindividual is female, P(Mobile Phone|Female). In the illustratedexample, the baseline statistics determiner 320 determines a value forP(Male) by processing the population attributes stored in the exampletarget population attributes storage 315 to count the number ofindividuals who are male, and then dividing by the total number ofindividuals in the sample population. In other words, the baselinestatistics determiner 320 determines a value for P(Male) by processingthe population attributes stored in the example target populationattributes storage 315 to determine a percentage of the individuals whoare male. Similarly, the example baseline statistics determiner 320determines a value for P(Female) by processing the population attributesstored in the example target population attributes storage 315 todetermine a percentage of the individuals who are female. The examplebaseline statistics determiner 320 determines a value for P(MobilePhone|Male) by processing the population attributes stored in theexample target population attributes storage 315 to determine thepercentage of individuals who are both male and are mobile phone users,and then dividing by the value of P(Male). Similarly, the examplebaseline statistics determiner 320 determines a value for P(MobilePhone|Female) by processing the population attributes stored in theexample target population attributes storage 315 to determine thepercentage of individuals who are both female and are mobile phoneusers, and then dividing by the value of P(Female). The example baselinestatistics determiner 320 determines a value for P(Male|Mobile Phone) byprocessing the population attributes stored in the example targetpopulation attributes storage 315 to determine the percentage ofindividuals who are male and mobile phone users and to determine thepercentage of individuals who are mobile phone users, and then dividingthe first value by the second value. Similarly, the example baselinestatistics determiner 320 determines a value for P(Female|Mobile Phone)by processing the population attributes stored in the example targetpopulation attributes storage 315 to determine the percentage ofindividuals who are female and mobile phone users and to determine thepercentage of individuals who are mobile phone users, and then dividingthe first value by the second value. Of course, additional oralternative statistics could be included in the set of baselinesdemographic statistics determined by the baseline statistics determiner320.

TABLE 1 Baseline Target Sample Demographic Statistic Population ValuePopulation Value P(Male) 34.78% 75.02% P(Female) 65.22% 24.98% P(Male |Mobile Phone) 27.12% 74.99% P(Female | Mobile Phone) 72.88% 25.01%P(Mobile Phone | Male) 40.61% 20.60% P(Mobile Phone | Female) 58.20%20.63%

Table 1 also illustrates a corresponding set of baseline demographicstatistics (corresponding to the third column of the table) capable ofbeing determined for a sample population from the population attributesstored in the example sample population attributes storage 310. In theillustrated example of Table 1, the set of baseline demographicstatistics for the sample population is determined without the benefitof bias correction as disclosed herein. Accordingly, the example firstset of baseline demographic statistics listed in Table 1 for the samplepopulation exhibits bias relative to the corresponding example secondset of baseline demographic statistics listed in Table 1 for the targetpopulation. For example, the two sets of baseline demographic statisticsindicate that the sample population includes an oversampling of malesand an undersampling of females relative to the target population.

To implement rating bias correction in accordance with the teachings ofthis disclosure, the example ratings bias corrector 120 of FIG. 3includes an example population strata partitioner 325, an example stratastatistics determiner 330 and an example strata weight determiner 335.The population strata partitioner 325 of the illustrated exampleaccesses information specifying a partition of the sample population(e.g., which includes the individuals for which population attributesare stored in the example sample population attributes storage 310) intoa set of population strata (also referred to as population partitions)having strata demographic statistics capable of being combined todetermine a set of baseline demographic statistics for the samplepopulation that corresponds to the set of baseline demographicstatistics determined by the baseline statistics determiner 320 for thetarget population. In the illustrated example of FIG. 3, for a given setof population strata specified in information accessed by the populationstrata partitioner 325, the example strata statistics determiner 330processes the population attributes stored in the example samplepopulation attributes storage 310 to determine the strata demographicstatistics for respective ones of the set of population strata. In someexamples, the strata demographic statistics represent numbers ofindividuals from the sample population belonging to respective stratumin the set of population strata. Accordingly, in such examples, todetermine the strata demographic statistic for a particular populationstratum, the strata statistics determiner 330 processes the populationattributes stored in the example sample population attributes storage310 to count the number of sample population individuals belonging tothe particular stratum.

In some examples, the set of population strata specified in theinformation accessed by the population strata partitioner 325 is amutually exclusive set (meaning that an individual of the samplepopulation belongs to only one of the strata) that covers the samplepopulation (meaning that strata, in combination, include all individualsof the sample population). Furthermore, in some examples, this set ofpopulation strata is a minimum set of mutually exclusive strata or, inother words, the set of population strata includes the fewest number ofstrata that are mutually exclusive and capable of covering the samplepopulation. For example, if the strata demographic statistics representnumbers of individuals from the sample population belonging torespective stratum in the set of population strata, then the populationstrata partitioner 325 accesses information specifying a set of strata(e.g., such as a minimum set of mutually exclusive strata) such that thestrata demographic statistics representing the numbers of samplepopulation individuals belonging to the different strata can be combined(e.g., in various combinations) to yield a set of baseline demographicsstatistics of the sample population that corresponds to the set ofbaseline demographic statistics determined by the baseline statisticsdeterminer 320 for the target population. In some examples, thepopulation strata partitioner 325 obtains the information specifying aset of strata capable of being used to determine a particular set ofbaseline demographics statistics from configuration information enteredby a user to specify the set of strata. In some examples, the populationstrata partitioner 325 utilizes one or more set partitioning algorithmsto determine the information specifying the set of strata.

Table 2 illustrates an example set of strata capable of being specifiedin information accessed by the example population strata partitioner 325for the example set of baseline demographic statistics listed inTable 1. The example set of strata represented in Table 2 include thefollowing four (4) strata: a first stratum including males who are notmobile phone users and having a corresponding strata demographicstatistic labeled “A” in the table, a second stratum including males whoare mobile phone users and having a corresponding strata demographicstatistic labeled “B” in the table, a third stratum including femaleswho are not mobile phone users and having a corresponding stratademographic statistic labeled “C” in the table, and a fourth stratumincluding females who are mobile phone users and having a correspondingstrata demographic statistic labeled “D” in the table.

TABLE 2 Strata Strata Statistic Male + Not Mobile Phone User A Male +Mobile Phone User B Female + Not Mobile Phone User C Female + MobilePhone User D

Table 3 illustrates an example set of strata demographic statisticscapable of being determined by the strata statistics determiner 330 forthe example set of strata listed in Table 2. In the example of Table 3,the strata demographic statistics correspond to the respective numbersof individuals of the sample population belonging to the differentstrata.

TABLE 3 Strata Strata Statistic (Subpopulation Size) Male + Not MobilePhone User A = 595,622 individuals Male + Mobile Phone User B = 154,530individuals Female + Not Mobile Phone User C = 198,315 individualsFemale + Mobile Phone User D = 51,533 individuals

Table 4 illustrates how the example set of baseline demographicstatistics listed in Table 1 can be determined for the sample populationfrom the example set of strata demographic statistics listed in Table 3for the example set of strata listed in Table 2. The example of Table 4illustrates that the example set of baseline demographic statisticslisted in Table 1 can be determined for the sample population from theexample set of strata demographic statistics using mathematicalexpressions involving division of a numerator term by a denominatorterm. The numerator and denominator terms for the different baselinedemographic statistics in Table 1 are computed from different ones orcombinations of the strata demographic statistics as shown in Table 4.

TABLE 4 Baseline Demographic Statistic Numerator Denominator P(Male) A +B A + B + C + D P(Female) C + D A + B + C + D P(Male | Mobile Phone) BB + D P(Female | Mobile Phone) D B + D P(Mobile Phone | Male) B A + BP(Mobile Phone | Female) D C + D

The example ratings bias corrector 120 of FIG. 3 further includes theexample strata weight determiner 335 to determine a set of weightscorresponding respectively to the set of population strata. In theillustrated example of FIG. 3, the strata weight determiner 335determines the set of weights to reduce bias between the set of baselinedemographic statistics determined by the baseline statistics determiner320 for the target population and the set of baseline demographicstatistics for the sample population, when the set of weights is appliedto the strata demographic statistics to determine the set of baselinedemographic statistics for the sample population. In other words, thestrata weight determiner 335 of the illustrated example determines theset of weights corresponding respectively to the set of populationstrata such that, when the respective weights are applied to (e.g., usedto scale) the strata demographic statistics of the respective populationstrata, the bias between the set of baseline demographic statisticscapable of being determined from the weighted, strata demographicstatistics (e.g., according to the example expressions represented inTable 4) and the set of baseline demographic statistics determined bythe baseline statistics determiner 320 for the target population isreduced (e.g., is minimized).

An example implementation of the strata weight determiner 335 of FIG. 3is illustrated in FIG. 4. The example strata weight determiner 335 ofFIG. 4 determines the set of weights for a set of population stratabased on a matrix, C, having elements that correspond to differentpossible pairings of ones of the strata demographic statistics and onesof the set of baseline demographic statistics. For example, such amatrix, C, has matrix elements (C_(ij)), where the variable i is anindex over the set of baseline demographic statistics (e.g., such as theexamples of Tables 1 and/or 4), and j is an index over the set ofpopulation strata (e.g., such as the examples of Tables 2 and/or 3). Insome such examples, the matrix element (C_(ij)) for a given i and jcorresponds to a pairing of the i^(th) strata demographic statistic andthe j^(th) baseline demographic statistic, and has a value determinedbased on a contribution of the i^(th) strata demographic statistic tothe j^(th) baseline demographic statistic (e.g., corresponding to theexample of Table 4). As such, the matrix, C, is also referred to hereinas the strata contribution matrix.

Accordingly, the example strata weight determiner 335 of FIG. 4 includesan example strata contribution matrix determiner 405 to form the stratacontribution matrix, C. In the example of FIG. 4, the stratacontribution matrix determiner 405 determines the strata contributionmatrix, C, based on the set of population strata, the corresponding setof strata demographic statistics, the set of baseline demographicstatistics, and the values of the set of baseline demographic statisticsfor the target population. For example, the strata contribution matrixdeterminer 405 can determine the matrix elements, (C_(ij)), of thestrata contribution matrix, C, according to Equations 1, which is:

(C _(ij))=k _(i)×([N _(ij)]−p _(i)[D _(ij)])λS _(j)   Equation 1

In Equation 1, i is an index over the baseline demographic statistics, jis an index over the population strata, k_(i) is an importance weightfor the i^(th) baseline demographic statistic (e.g., having a value of 1or some other value), [N_(ij)] equals 1 if the i^(th) baselinedemographic statistic can be calculated using the j^(th) stratademographic statistic as a numerator term and equals 0 otherwise (e.g.,according to the example of Table 4), [D_(ij)] equals 1 if the i^(th)baseline demographic statistic can be calculated using the j^(th) stratademographic statistic as a denominator term and equals 0 otherwise(e.g., according to the example of Table 4), p_(i) is value of thei^(th) baseline demographic statistic for the target population (e.g.,such as the values included in the second column of the example of Table1), and S_(j) is value of the strata demographic statistic for thej^(th) population strata of the sample population (e.g., such as thenumber of individuals in the sample population belonging to the j^(th)strata, for example, the values included in the example of Table 3).

Table 5 illustrates different possible values for the inner term([N_(ij)]−p_(i)[D_(ij)]) of Equation 1 depending on whether the j^(th)strata demographic statistic appears in the numerator term, thedenominator term, both the numerator and denominator terms, or neitherof the numerator and denominator terms used to calculate the i^(th)baseline demographic statistic for the sample population from the set ofstrata demographic statistic (e.g., according to Table 4, as anexample).

TABLE 5 Value of ([N_(ij)] − p_(j)[D_(ij)]) from Equation 1 ConditionCausing Listed Value 0 j^(th) strata demographic statistic in neitherthe numerator nor the denominator terms used to calculate i^(th)baseline demographic statistic for the sample population 1 j^(th) stratademographic statistic in only the numerator term used to calculatei^(th) baseline demographic statistic for the sample population −p_(i)j^(th) strata demographic statistic in only the denominator term used tocalculate i^(th) baseline demographic statistic for the samplepopulation 1 − p_(i) j^(th) strata demographic statistic in both thenumerator and the denominator terms used to calculate i^(th) baselinedemographic statistic for the sample population

As mentioned above, the value of k, in Equation 1 is an importanceweight for the i^(th) baseline demographic statistic. In some examples,the value of k_(i) defaults to a value of 1. In some examples, the valueof k_(i) is adjustable (e.g., based on a user configuration input) to,for example, emphasize or deemphasize the importance of one or more ofthe strata and/or one or more of the baseline demographic statistics inthe computation of the set of weights.

The example strata weight determiner 335 of FIG. 4 also includes anexample strata weight solver 410 to determine the set of weights to beused to scale the strata demographic statistics (and/or other populationattributes) associated with the set of population strata. In theillustrated example of FIG. 4, the strata weight solver 410 determinesthe set of weights, (w_(j)), for the set of population strata, j, basedon the strata contribution matrix, C, determined by the stratacontribution matrix determiner 405. In some examples, the strata weightsolver 410 determines the set of weights to minimize an expression basedon multiplying the matrix strata contribution matrix, C, and a vector,w=(w_(j)), of the weights subject to a constraint that the weights havevalues greater than or equal to one. An example of such an expression,L, is illustrated by Equation 2, which is:

minimize L=∥C×∥w ₂ ² subject to 1≤(w _(j))<∞   Equation 2

In Equation 2, the expression, L, corresponds to the squared L2 norm ofthe strata contribution matrix, C, and the strata weight vector,w=(w_(j)), but subject to a constraint that the weights have valuesgreater than or equal to one. In some examples, the strata weight solver410 implements Equation 2 by arranging the strata weights as the vector,w=(w_(j)), and then determining the values, w₁, of the weights bysolving, using any appropriate numerical technique, a constrained linearleast squares problem defined by the expression, L, and the constraintgiven in Equation 2.

In some examples, the strata weight determiner 335 determines thevalues, w_(j), of the strata weights by solving, using any appropriatenumerical technique, a linear least squares problem defined by theexpression L of Equation 2, but without the constraint that the weightshave values greater than or equal to one, or with a different constrainton or scaling of the weights. This is because the relative values,w_(j), of the weights for different strata, not the actual values ofthese weights, yield the desired bias reduction. For example, for three(3) strata indexed j=1, 2, 3, weight values (w₁, w₂, w₃) of (1, 3, 10),(2, 6, 20), (100, 300, 1000) or (0.1, 0.3, 1) might yield the same biasreduction because these different sets of weights have the same relativerelationships between different strata (e.g., w₂=3×w₁ and w₃=10×w₁ foreach of these example sets of strata weights). However, in someexamples, it may be convenient to constrain the weights to have valuesgreater than or equal to one (e.g., according to Equation 2), or someother value (e.g., by replacing the value “1” in Equation 2 with anothervalue).

Table 6 illustrates an example set of strata weights, w=(w_(j)), j=1, .. . , 4, capable of being determined by the strata weight solver 410 forthe example set of strata, j=1, . . . , 4, and corresponding examplestrata demographic statistics listed in Table 3.

TABLE 6 Strata Statistic Strata Strata (Subpopulation Size) WeightMale + Not Mobile Phone User A = 595,622 individuals 1.000 Male + MobilePhone User B = 154,530 individuals 2.636 Female + Not Mobile Phone UserC = 198,315 individuals 3.964 Female + Mobile Phone User D = 51,533individuals 21.240Table 7 illustrates that the example strata weights listed in Table 6can correct for the bias between the set of baseline demographicstatistics for the target population and the corresponding set ofbaseline demographic statistics determined for the target populationfrom the strata demographic statistics.

TABLE 7 Baseline Target Sample Population Sample Population DemographicPopulation Value (Without Value (With Statistic Value Weighting)Weighting) P(Male) 34.78% 75.02% 34.78% P(Female) 65.22% 24.98% 65.22%P(Male | 27.12% 74.99% 27.12% Mobile Phone) P(Female | 72.88% 25.01%72.88% Mobile Phone) P(Mobile 40.61% 20.60% 40.61% Phone | Male)P(Mobile 58.20% 20.63% 58.20% Phone | Female)For example, with reference to Table 4, the value of the last baselinedemographic statistic listed in Table 7 (i.e., P(Mobile Phone|Female))is calculated for the sample population from the strata statisticslisted in Tables 2 and 3 using Equation 3, which is:

${P\left( {{Mobile}\mspace{14mu} {Phone}} \middle| {Female} \right)} = \frac{D}{C + D}$

Evaluating Equation 3 without weighting yields

${P\left( {{Mobile}\mspace{14mu} {Phone}} \middle| {Female} \right)} = {\frac{51\text{,}533}{{198\text{,}315} + {51\text{,}533}} = {2{0.6}3\%}}$

which is the sample population value (without weighting) listed in Table7. However, evaluating Equation 3 with weighting using the weights ofTable 6 yields

${P\left( {{Mobile}\mspace{14mu} {Phone}} \middle| {Female} \right)} = {\frac{51\text{,}533 \times 2{1.2}40}{\begin{matrix}{{198\text{,}315 \times 3.9640} +} \\{51\text{,}533 \times 2{1.2}40}\end{matrix}} = {5{8.2}0\%}}$

which is the sample population value (with weighting) listed in Table 7.

Returning to FIG. 3, the example ratings bias corrector 120 illustratedtherein also includes an example ratings determiner 340 to adjust, basedon the set of weights determined by the example strata weight determiner235, attributes associated with individuals of the sample population(e.g., as stored in the example sample population attributes storage310) to determine ratings data. For example, the ratings determiner 340can use the weights determined by the example strata weight determiner235 to scale the contribution of attribute(s) for an individual in thesample population when determining ratings data associated with theattribute(s) to account for the bias between the sample population andthe target population. In some examples, if a first individual of thesample population is included in a first one of the population strata,and a second individual of the sample population is included in a secondone of the population strata, the ratings determiner 340 adjusts theattributes associated with the individuals (e.g., the first and secondindividual) of sample population to determine the ratings data byscaling a contribution of an attribute associated with the firstindividual by a first one of the weights corresponding to the firstpopulation strata to determine a first scaled attribute contribution. Insuch examples, the ratings determiner 340 also scales a contribution ofan attribute associated with the second individual by a second one ofthe weights corresponding to the second population strata to determine asecond scaled attribute contribution. In such examples, the ratingsdeterminer 340 further combines (e.g., adds, multiples, etc.) the firstscaled attribute contribution and the second scaled attributecontribution to determine the ratings data.

For example, and with reference to the example of Table 6, if anattribute corresponds to whether an individual was exposed to givenmedia, the ratings determiner 340 may scale (e.g., or, in other words,emphasize or de-emphasize) the contribution (e.g., presence of absenceof exposure to the given media) of a first individual from the firststrata (e.g., Male+Not Mobile Phone User) by the weight for that firststrata (e.g., w₁=1.000) and may scale (e.g., or, in other words,emphasize or de-emphasize) the contribution (e.g., presence of absenceof exposure to the given media) of a second individual from the secondstrata (e.g., Male+Mobile Phone User) by the weight for that secondstrata (e.g., w₂=2.636) when determining the ratings data quantifyingviewership of the given media. In other words, in such an example, theratings determiner 340 may count the first individual's exposure (orlack thereof) to the given media as corresponding to 1.000 individualshaving the same demographic characteristics as the first individual, butmay count the second individual's exposure (or lack thereof) to thegiven media as corresponding to 2.636 individuals having the samedemographic characteristics as the second individual.

The example ratings bias corrector 120 of FIG. 3 further includes anexample ratings data reporter 345 to report the ratings data determinedby the example ratings determiner 340 to one or more recipients. In someexamples, the ratings data reporter 345 transmits the ratings dataelectronically and/or optically from the ratings bias corrector 120 to areceiving device via one or more communication networks. As such, insome examples, the example ratings data reporter 345 can be implementedby any type(s), number(s) and/or combination(s) of communicationinterfaces, network interfaces, etc., such as the example interfacecircuit 820 of FIG. 8, which is described in further detail below.

Although the example ratings bias corrector 120 of FIGS. 1-4 has beendescribed primarily from the perspective of determining ratings databased on logged media impressions for online media, the example methods,apparatus, systems and articles of manufacture (e.g., physical storagemedia) disclosed herein to correct for bias in ratings data havinginterdependencies among demographic statistics are not limited thereto.On the contrary, the example ratings bias corrector 120 can determineratings data from any type of population sample data havinginterdependencies among demographic statistics. For example, the exampleratings bias corrector 120 can determine ratings data for populationsample data that logs and/or otherwise represents population attributessuch as, but not limited to, media impressions, products purchased,services accessed, etc.

While example manners of implementing the ratings bias corrector 120 isillustrated in FIGS. 1-4, one or more of the elements, processes and/ordevices illustrated in FIGS. 1-4 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample data interface 305, the example sample population attributesstorage 310, the example target population attributes storage 315, theexample baseline statistics determiner 320, the example populationstrata partitioner 325, the example strata statistics determiner 330,the example strata weight determiner 335, the example ratings determiner340, the example ratings data reporter 345, the example stratacontribution matrix determiner 405, the example strata weight solver 410and/or, more generally, the example ratings bias corrector 120 of FIGS.1-4 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example data interface 305, the example sample populationattributes storage 310, the example target population attributes storage315, the example baseline statistics determiner 320, the examplepopulation strata partitioner 325, the example strata statisticsdeterminer 330, the example strata weight determiner 335, the exampleratings determiner 340, the example ratings data reporter 345, theexample strata contribution matrix determiner 405, the example strataweight solver 410 and/or, more generally, the example ratings biascorrector 120 could be implemented by one or more analog or digitalcircuit(s), logic circuits, programmable processor(s), applicationspecific integrated circuit(s) (ASIC(s)), programmable logic device(s)(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). Whenreading any of the apparatus or system claims of this patent to cover apurely software and/or firmware implementation, at least one of theexample ratings bias corrector 120, the example data interface 305, theexample sample population attributes storage 310, the example targetpopulation attributes storage 315, the example baseline statisticsdeterminer 320, the example population strata partitioner 325, theexample strata statistics determiner 330, the example strata weightdeterminer 335, the example ratings determiner 340, the example ratingsdata reporter 345, the example strata contribution matrix determiner 405and/or the example strata weight solver 410 is/are hereby expresslydefined to include a tangible computer readable storage device orstorage disk such as a memory, a digital versatile disk (DVD), a compactdisk (CD), a Blu-ray disk, etc. storing the software and/or firmware.Further still, the example ratings bias corrector 120 may include one ormore elements, processes and/or devices in addition to, or instead of,those illustrated in FIGS. 1-4, and/or may include more than one of anyor all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example ratings bias corrector 120, the example datainterface 305, the example sample population attributes storage 310, theexample target population attributes storage 315, the example baselinestatistics determiner 320, the example population strata partitioner325, the example strata statistics determiner 330, the example strataweight determiner 335, the example ratings determiner 340, the exampleratings data reporter 345, the example strata contribution matrixdeterminer 405 and/or the example strata weight solver 410 are shown inFIGS. 5-7. In these examples, the machine readable instructions compriseone or more programs for execution by a processor, such as the processor812 shown in the example processor platform 800 discussed below inconnection with FIG. 8. The one or more programs, or portion(s) thereof,may be embodied in software stored on a tangible computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk™, or a memory associated with theprocessor 812, but the entire program or programs and/or portionsthereof could alternatively be executed by a device other than theprocessor 812 and/or embodied in firmware or dedicated hardware (e.g.,implemented by an ASIC, a PLD, an FPLD, discrete logic, etc.). Further,although the example program(s) is(are) described with reference to theflowcharts illustrated in FIGS. 5-7, many other methods of implementingthe example ratings bias corrector 120, the example data interface 305,the example sample population attributes storage 310, the example targetpopulation attributes storage 315, the example baseline statisticsdeterminer 320, the example population strata partitioner 325, theexample strata statistics determiner 330, the example strata weightdeterminer 335, the example ratings determiner 340, the example ratingsdata reporter 345, the example strata contribution matrix determiner 405and/or the example strata weight solver 410 may alternatively be used.For example, with reference to the flowcharts illustrated in FIGS. 5-7,the order of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, combined and/or subdividedinto multiple blocks.

As mentioned above, the example processes of FIGS. 5-7 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 5-7 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a ROM, a CD,a DVD, a cache, a RAM and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the terms“comprising” and “including” are open ended. Also, as used herein, theterms “computer readable” and “machine readable” are consideredequivalent unless indicated otherwise.

An example program 500 that may be executed to implement the exampleratings bias corrector 120 of FIGS. 1-4 is represented by the flowchartshown in FIG. 5. With reference to the preceding figures and associatedwritten descriptions, the example program 500 of FIG. 5 begins executionat block 505 at which the example baseline statistics determiner 320 ofthe ratings bias corrector 120 determines a set of baseline demographicstatistics for a target population, as disclosed above. At block 510,the example population strata partitioner 325 of the ratings biascorrector 120 accesses, as disclosed above, information specifying apartition of individuals of a sample population into a set of populationstrata having corresponding strata demographic statistics capable ofbeing combined to determine a set of baseline demographic statistics forthe sample population that corresponds to the set of baselinedemographic statistics obtained at block 505 for the target population.

At block 515, the example strata statistics determiner 330 and theexample strata weight determiner 335 of the ratings bias corrector 120determine, as disclosed above, a set of strata weights for the set ofpopulation strata obtained at block 515. The set of strata weightsdetermined at block 515 reduces bias between the set of baselinedemographic statistics obtained at block 505 for the target populationand a corresponding set of baseline demographic statistics capable ofbeing determined from strata demographic statistics associated with thestrata obtained at block 510. An example program capable of performingthe processing at block 515 is illustrated in FIG. 6, which is describedin further detail below.

At block 520, the example ratings determiner 340 of the ratings biascorrector 120 adjusts, based on the set of weights obtained at block 515and as disclosed above, attributes associated with individuals of thesample population to determine ratings data. An example program capableof performing the processing at block 520 is illustrated in FIG. 7,which is described in further detail below. At block 525, the exampleratings data reporter 345 of the ratings bias corrector 120 reports theratings data obtained at block 520 to one or more recipients, asdisclosed above.

An example program P515 that may be executed to implement the examplestrata statistics determiner 330 of FIG. 3 and/or the example strataweight determiner 335 of FIGS. 3 and/or 4, and/or to perform theprocessing at block 515 of FIG. 5, is represented by the flowchart shownin FIG. 6. With reference to the preceding figures and associatedwritten descriptions, the example program P515 of FIG. 6 beginsexecution at block 605 at which the example strata statistics determiner330 determines, as disclosed above, strata demographic statistics for arespective set of population strata into which a sample population hasbeen partitioned. At block 610, the example strata contribution matrixdeterminer 405 of the strata weight determiner 335 forms, as disclosedabove, the strata contribution matrix, C, which has elements havingvalues (e.g., corresponding to Equation 1) representing contributions ofones of the strata demographic statistics obtained at block 605 to onesof a set of baseline demographic statistics capable of being determinedfor the sample population (e.g., and which correspond to a set ofbaseline demographic statistics known for a target population). At block615, the example strata weight solver 410 of the strata weightdeterminer 335 determines, as disclosed above, a set of strata weightscorresponding to the set of strata into which the sample population hasbeen partitioned. At block 615, the example strata weight solver 410determines the set of weights to reduce (e.g., minimize) an expression(e.g., corresponding to Equation 2) based on multiplying the stratacontribution matrix, C, by a vector, w, of the weights, and subject to aconstraint that the weights have values greater than or equal to one. Inthe illustrated example, the program P515 uses the strata contributionmatrix, C, to determine the weights, w, in a single pass or, in otherwords, without the need to perform multiple processing iterations toadjust the weights, w, to correct for errors (e.g., increased biases)introduced by prior processing iterations.

An example program P520 that may be executed to implement the exampleratings determiner 340 of FIG. 3, and/or to perform the processing atblock 520 of FIG. 5, is represented by the flowchart shown in FIG. 7.With reference to the preceding figures and associated writtendescriptions, the example program P520 of FIG. 7 begins execution atblock 705 at which the ratings determiner 340 accesses (e.g., from theexample sample population attributes storage 310) one or more attributesfor respective individuals of the sample population. At block 710, theratings determiner 340 scales, or otherwise adjusts, the contributionsof the attribute(s) accessed for respective individuals of the samplepopulation using the set of strata weights determined for the set ofpopulation strata into which the sample population has been partitioned,as disclosed above. For example, at block 710, the ratings determiner340 scales the contribution of an attribute associated with a firstindividual of the sample population by the particular strata weightcorresponding to the particular population stratum in which the firstindividual belongs. Similarly, at block at block 710, the ratingsdeterminer 340 scales the contribution of an attribute associated with asecond individual of the sample population by the particular strataweight corresponding to the particular population stratum in which thesecond individual belongs (which may be same as or different from thepopulation stratum in which the first individual belongs). At block 715,the ratings determiner 340 combines, as disclosed above, the scaled (orotherwise adjusted) attribute contributions determined at block 710 forthe individual of the sample population to determine ratings datarepresentative of the sample population.

FIG. 8 is a block diagram of an example processor platform 800 capableof executing the instructions of FIGS. 5, 6 and/or 7 to implement theexample ratings bias corrector 120 of FIGS. 1-4. The processor platform800 can be, for example, a server, a personal computer, a mobile device(e.g., a cell phone, a smart phone, a tablet such as an iPad′), apersonal digital assistant (PDA), an Internet appliance, a DVD player, aCD player, a digital video recorder, a Blu-ray player, a gaming console,a personal video recorder, a set top box a digital camera, or any othertype of computing device.

The processor platform 800 of the illustrated example includes aprocessor 812. The processor 812 of the illustrated example is hardware.For example, the processor 812 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer. In the illustrated example of FIG.8, the processor 812 includes one or more example processing cores 815configured via example instructions 832, which include the exampleinstructions of FIGS. 5, 6 and/or 7, to implement the example baselinestatistics determiner 320, the example population strata partitioner325, the example strata statistics determiner 330, the example strataweight determiner 335, the example ratings determiner 340, the examplestrata contribution matrix determiner 405 and/or the example strataweight solver 410 of FIGS. 3 and/or 4.

The processor 812 of the illustrated example includes a local memory 813(e.g., a cache). The processor 812 of the illustrated example is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a link 818. The link 818 may be implementedby a bus, one or more point-to-point connections, etc., or a combinationthereof. The volatile memory 814 may be implemented by SynchronousDynamic Random Access Memory (SDRAM), Dynamic Random Access Memory(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any othertype of random access memory device. The non-volatile memory 816 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the main memory 814, 816 is controlled by a memorycontroller.

The processor platform 800 of the illustrated example also includes aninterface circuit 820. The interface circuit 820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 822 are connectedto the interface circuit 820. The input device(s) 822 permit(s) a userto enter data and commands into the processor 812. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, a trackbar (such as an isopoint), a voicerecognition system and/or any other human-machine interface. Also, manysystems, such as the processor platform 800, can allow the user tocontrol the computer system and provide data to the computer usingphysical gestures, such as, but not limited to, hand or body movements,facial expressions, and face recognition.

One or more output devices 824 are also connected to the interfacecircuit 820 of the illustrated example. The output devices 824 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 820 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network826 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.). Inthe illustrated example of FIG. 8, the interface circuit 820 is alsostructured to implement one or more of the example data interface 305and/or the example ratings data reporter 345 of FIG. 3.

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 828 for storing software and/or data.Examples of such mass storage devices 828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAID(redundant array of independent disks) systems, and digital versatiledisk (DVD) drives. In some examples, the mass storage device 830 mayimplement the example sample population attributes storage 310 and/orthe example target population attributes storage 315. Additionally oralternatively, in some examples the volatile memory 818 may implementthe example sample population attributes storage 310 and/or the exampletarget population attributes storage 315.

Coded instructions 832 corresponding to the instructions of FIGS. 5, 6and/or 7 may be stored in the mass storage device 828, in the volatilememory 814, in the non-volatile memory 816, in the local memory 813and/or on a removable tangible computer readable storage medium, such asa CD or DVD 836.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: memory; and a processorto execute computer readable instructions to: process messages receivedfrom a plurality of devices via a network to determine attribute dataassociated with a sample population; determine a plurality of weightsbased on a data structure having elements corresponding to pairings ofones of a plurality of demographic partition statistics and ones of aplurality of baseline demographic statistics obtained for a targetpopulation, the demographic partition statistics corresponding to aplurality of demographic partitions of the sample population, thedemographic partition statistics based on the attribute data, a firstelement of the data structure to combine a first one of the demographicpartition statistics with a first one of the baseline demographicstatistics of the target population based on a first value and a secondvalue, the first value based on a numerator term of an expression tocalculate a corresponding first one of the baseline demographicstatistics of the sample population based on the demographic partitionstatistics, the second value based on a denominator term of theexpression, the weights corresponding respectively to the demographicpartitions of the sample population; and adjust the attribute data basedon the weights to determine ratings data for the target population. 2.The apparatus of claim 1, wherein the data structure is a matrix.
 3. Theapparatus of claim 1, wherein respective ones of the demographicpartition statistics are based on respective numbers of individuals inthe sample population that belong to corresponding ones of thedemographic partitions.
 4. The apparatus of claim 3, wherein theprocessor is to determine the demographic partitions to form a mutuallyexclusive set that covers the sample population.
 5. The apparatus ofclaim 1, wherein the first one of the baseline demographic statistics ofthe target population is based on a first number of individuals in thetarget population that have attributes that correspond to a first one ofa plurality of different demographic classifications.
 6. The apparatusof claim 5, wherein the corresponding first one of the baselinedemographic statistics of the sample population is based on a secondnumber of individuals in the sample population that have attributes thatcorrespond to the first one of the different demographicclassifications, and the first one of the baseline demographicstatistics of the sample population is also computable from thedemographic partition statistics based on the expression.
 7. Theapparatus of claim 1, wherein the processor is to: set the first valueto one or zero based on whether the first one of the demographicpartition statistics is included in the numerator term of theexpression; and set the second value to one or zero based on whether thefirst one of the demographic partition statistics is included in thedenominator term of the expression.
 8. The apparatus of claim 7, whereinthe processor is to determine the first element of the data structurebased on the first one of the demographic partition statistics, thefirst one of the baseline demographic statistics of the targetpopulation, the first value, the second value and a third value, thethird value based on an importance of the first one of the baselinedemographic statistics relative to other ones of the baselinedemographic statistics.
 9. A non-transitory computer readable mediumcomprising computer readable instructions that, when executed, cause aprocessor to at least: process messages from a plurality of devices todetermine attribute data associated with a first population; determine aplurality of weights based on a matrix having elements corresponding topairings of ones of a plurality of demographic partition statistics andones of a plurality of baseline demographic statistics obtained for asecond population, the demographic partition statistics corresponding toa plurality of demographic partitions of the first population, thedemographic partition statistics based on the attribute data, a firstelement of the matrix to combine a first one of the demographicpartition statistics with a first one of the baseline demographicstatistics of the second population based on a first value and a secondvalue, the first value based on a numerator term of an expression tocalculate a corresponding first one of the baseline demographicstatistics of the first population based on the demographic partitionstatistics, the second value based on a denominator term of theexpression, the weights corresponding respectively to the demographicpartitions of the first population; and adjust the attribute data basedon the weights to determine ratings data for the second population. 10.The non-transitory computer readable medium of claim 9, whereinrespective ones of the demographic partition statistics are based onrespective numbers of individuals in the first population that belong tocorresponding ones of the demographic partitions.
 11. The non-transitorycomputer readable medium of claim 9, wherein the first one of thebaseline demographic statistics of the second population is based on afirst number of individuals in the second population that haveattributes that correspond to a first one of a plurality of differentdemographic classifications.
 12. The non-transitory computer readablemedium of claim 11, wherein the corresponding first one of the baselinedemographic statistics of the first population is based on a secondnumber of individuals in the first population that have attributes thatcorrespond to the first one of the different demographicclassifications, and the first one of the baseline demographicstatistics of the first population is also computable from thedemographic partition statistics based on the expression.
 13. Thenon-transitory computer readable medium of claim 9, wherein theinstructions cause the processor to: set the first value to one or zerobased on whether the first one of the demographic partition statisticsis included in the numerator term of the expression; and set the secondvalue to one or zero based on whether the first one of the demographicpartition statistics is included in the denominator term of theexpression.
 14. The non-transitory computer readable medium of claim 13,wherein the instructions cause the processor to determine the firstelement of the matrix based on the first one of the demographicpartition statistics, the first one of the baseline demographicstatistics of the second population, the first value, the second valueand a third value, the third value based on an importance of the firstone of the baseline demographic statistics relative to other ones of thebaseline demographic statistics.
 15. A system comprising: means forprocessing beacon messages from a plurality of client devices todetermine impression data for a sample population associated with theclient devices; and means for determining ratings data for a targetpopulation, the means for determining the ratings data to: determine aplurality of weights based on a matrix having elements corresponding topairings of ones of a plurality of strata statistics and ones of aplurality of baseline demographic statistics obtained for a targetpopulation, the strata statistics corresponding to a plurality ofdemographic strata of the sample population, the strata statistics basedon attribute data associated with the impression data, a first elementof the matrix to combine a first one of the strata statistics with afirst one of the baseline demographic statistics of the targetpopulation based on a first value and a second value, the first valuebased on a numerator term of an expression to calculate a correspondingfirst one of the baseline demographic statistics of the samplepopulation based on the strata statistics, the second value based on adenominator term of the expression, the weights correspondingrespectively to the demographic strata of the sample population; andadjust the attribute data based on the weights to determine ratings datafor the target population.
 16. The system of claim 15, wherein the meansfor determining the ratings data is to determine the demographic stratato form a mutually exclusive set that covers the sample population, andrespective ones of the strata statistics are based on respective numbersof individuals in the sample population that belong to correspondingones of the demographic strata.
 17. The system of claim 15, wherein thefirst one of the baseline demographic statistics of the targetpopulation is based on a first number of individuals in the targetpopulation that have attributes that correspond to a first one of aplurality of different demographic classifications.
 18. The system ofclaim 17, wherein the corresponding first one of the baselinedemographic statistics of the sample population is based on a secondnumber of individuals in the sample population that have attributes thatcorrespond to the first one of the different demographicclassifications, and the first one of the baseline demographicstatistics of the sample population is also computable from the stratastatistics based on the expression.
 19. The system of claim 15, whereinthe means for determining the ratings data is to: set the first value toone or zero based on whether the first one of the strata statistics isincluded in the numerator term of the expression; and set the secondvalue to one or zero based on whether the first one of the stratastatistics is included in the denominator term of the expression. 20.The system of claim 19, wherein the means for determining the ratingsdata is to determine the first element of the matrix based on the firstone of the strata statistics, the first one of the baseline demographicstatistics of the target population, the first value, the second valueand a third value, the third value based on an importance of the firstone of the baseline demographic statistics relative to other ones of thebaseline demographic statistics.